Communications of the ACM - Special issue on parallelism
The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Prediction games and arcing algorithms
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning Weighted Metrics to Minimize Nearest-Neighbor Classification Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving nearest neighbor rule with a simple adaptive distance measure
Pattern Recognition Letters
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Large margin nearest neighbor classifiers
IEEE Transactions on Neural Networks
Coarse to fine K nearest neighbor classifier
Pattern Recognition Letters
Robust feature selection based on regularized brownboost loss
Knowledge-Based Systems
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The nearest neighbor classification is a simple and yet effective technique for pattern recognition. Performance of this technique depends significantly on the distance function used to compute similarity between examples. Some techniques were developed to learn weights of features for changing the distance structure of samples in nearest neighbor classification. In this paper, we propose an approach to learning sample weights for enlarging margin by using a gradient descent algorithm to minimize margin based classification loss. Experimental analysis shows that the distances trained in this way reduce the loss of the margin and enlarge the hypothesis margin on several datasets. Moreover, the proposed approach consistently outperforms nearest neighbor classification and some other state-of-the-art methods.